class_docstr: Class for Grassmann manifolds :math:`Gr(n, p)`.
  
  Parameters
  ----------
  n : int
      Dimension of the Euclidean space.
  p : int
      Dimension of the subspaces.
docstr: Sample random points from a uniform distribution.
  
  Following [Chikuse03]_, :math:`n\_samples * n * p` scalars are sampled
  from a standard normal distribution and reshaped to matrices,
  the projectors on their first :math:`p` columns follow a uniform
  distribution.
  
  Parameters
  ----------
  n_samples : int
      The number of points to sample
      Optional. default: 1.
  
  Returns
  -------
  projectors : array-like, shape=[..., n, n]
      Points following a uniform distribution.
  
  References
  ----------
  .. [Chikuse03] Yasuko Chikuse, Statistics on special manifolds,
      New York: Springer-Verlag. 2003, 10.1007/978-0-387-21540-2
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code: def random_point(self, n_samples=1, bound=1.0):
      r"""Sample random points from a uniform distribution.
  
      Following [Chikuse03]_, :math:`n\_samples * n * p` scalars are sampled
      from a standard normal distribution and reshaped to matrices,
      the projectors on their first :math:`p` columns follow a uniform
      distribution.
  
      Parameters
      ----------
      n_samples : int
          The number of points to sample
          Optional. default: 1.
  
      Returns
      -------
      projectors : array-like, shape=[..., n, n]
          Points following a uniform distribution.
  
      References
      ----------
      .. [Chikuse03] Yasuko Chikuse, Statistics on special manifolds,
          New York: Springer-Verlag. 2003, 10.1007/978-0-387-21540-2
      """
      return self.random_uniform(n_samples)
  
imports: import geomstats.backend
  import geomstats.errors
  from geomstats.geometry.base import LevelSet
  from geomstats.geometry.general_linear import GeneralLinear
  from geomstats.geometry.matrices import Matrices, MatricesMetric
  from geomstats.geometry.symmetric_matrices import SymmetricMatrices
  from geomstats.vectorization import repeat_out
label_desc: Use image node_id 7 for calling the Grassmannian obj's underlying member method code with example usage: obj.random_point(n_samples, bound) and returns: self